Using the outputs from our big-data platform, we have been able to deploy new artificial-intelligence capabilities, increasing our rates of automation for zipMoney underwriting decisions by 50%. We have also reduced operating expenses and the strain on technical resources.

Daniel BambagiottiHead of Analytics & Data Science

About zipMoney

zipMoney is an Australian fintech firm offering interest-free consumer loans through partner merchants as part of the growing “buy now, pay later” market. The startup went public in 2015 and received a record $260 million debt facility from a National Australia Bank less than two years later. Its innovations in big-data modeling—including artificial intelligence and machine-learning capabilities—have enabled more accurate analysis of factors that can predict credit or fraud risk.

As a leader in Australia’s “buy now, pay later” market, zipMoney provides seamless and responsible consumer-finance and payment experiences. Driven by its proprietary credit and fraud decision engine, retail customers get real-time, point-of-sale credit upon checkout with partner merchants. Stores in the zipMoney ecosystem enjoy increased average ticket value and increased transaction volume. The company’s prime differentiator is its ability to tap into and digest both conventional and unconventional data from sources such as social media, device fingerprint, and transactional bank accounts, to make underwriting decisions quickly. These data assets have allowed zipMoney to optimize approval rates and minimize loss rates versus more traditional approaches adopted by larger lenders.

zipMoney was born in the cloud with Amazon Web Services (AWS). The firm started by using Amazon Elastic Compute Cloud (Amazon EC2) to provide computing power, Amazon Simple Storage Service (Amazon S3) to store customer data, and Amazon Relational Database Service (Amazon RDS) to manage that data. However, as the company grew, the volume of data increased exponentially. In addition, as the company was able to tap into a wider variety of data from disparate sources, analytics became more time consuming, as semi-structured formats such as XML had to be flattened out before analysis. For example, each time a new customer applies for zipMoney financing via a smartphone or tablet, more than 200 pieces of device data are captured. The zipMoney team found standard SQL databases to be limiting, because they often needed to query ad hoc elements from these big sets of data.

Management began considering how to move to a serverless environment to further automate its underwriting process and fully utilize the huge amount of available data.

Three years after beginning operations, zipMoney was approached by other vendors of big-data solutions, but the decision to remain with AWS was ultimately based on communication. “We found the AWS team to be much more engaged and available to help us through the process. It’s important that we develop working relationships with every supplier and customer. With AWS, we’ve been able to pull in the right subject-matter experts whenever we hit roadblocks, which has enabled us to move forward quickly,” says Mike Greer, chief technology officer at zipMoney.

In 2016, zipMoney began working with AWS solutions architects to develop its own data lake, or big-data platform. Before migrating any data, the team attended AWS workshops on big-data architecture as well as security measures, which were paramount for the project. Today, zipMoney employs native encrypting options such as AWS Key Management Service (Amazon KMS), following AWS best practices.

To organize and more efficiently process data, zipMoney uses Amazon S3 buckets with Amazon Elastic MapReduce (Amazon EMR), Amazon DynamoDB, and Amazon Elasticsearch Service. The reliability and scalability of AWS Lambda (Lambda)—which enables customers to run code without provisioning or managing servers—for running code has been key to the system transformation, and Greer’s technology team sees Lambda as “a staple of its data architecture” going forward.

Daniel Bambagiotti, head of analytics and data science at zipMoney, has also seen dramatic improvements that have enabled his team to flatten out and analyze data across a variety of formats thanks to the overarching big-data technology. “We are able to query information in ways that we haven’t been able to do previously, or if we had been doing it, it would have been very time consuming,” he says.

“I’ve spent over 14 years in data science, and until now have not seen something developed this fast, which is a real credit to AWS and our team,” Bambagiotti says.

The company’s new big-data platform on AWS has enabled the rapid development of machine-learning models that have decreased analytical build times by more than 50 percent. This has resulted in faster deployment, as data scientists spend less time cleansing data and more time innovating to apply new quantitative methods that identify credit risk.

When faced with a questionable underwriting decision, there’s often a gray area where cases are typically referred to an underwriter. However, with zipMoney’s new analytical models, the company can significantly reduce manual referral rates without compromising on the quality of decision making. “Using the outputs from our big-data platform, we have been able to deploy new artificial-intelligence capabilities, increasing our rates of automation for zipMoney underwriting decisions by 50 percent. We have also reduced operating expenses and the strain on technical resources,” says Bambagiotti.

In addition to being cost effective, the zipMoney team was drawn to the performance and functionality of the AWS suite of solutions, particularly AWS Managed Services. “Managed services take away the complexity from much of our day-to-day work and allow us to more confidently scale with the huge volume increases we’ve seen,” says Greer.

The business is excited about the potential of taking advantage of new data insights, building more machine-learning algorithms to improve the customer experience, looking at new products, and simply adjusting its current product based on its findings. “Right now, we mainly cater to the millennial consumer,” says Bambagiotti. “But with this new depth of insight and analysis, we can further personalize offerings for all demographics by identifying who our customers really are and what drives them to purchasing decisions.” zipMoney management feels the company is just scratching the surface with how it can use all the data available through partner merchants, and is now looking into AWS Certification training for its staff.

“Our success is tied in with AWS,” Greer adds. “Rapid and reliable delivery, having the product up and running 24/7, and the elastic growth in infrastructure is key to customer happiness.”